This paper describes our method for tuning a transformer-based pretrained model, to adaptation with Reliable Intelligence Identification on Vietnamese SNSs problem. We also proposed a model that combines bert-base pretrained models with some metadata features, such as the number of comments, number of likes, images of SNS documents,... to improved results for VLSP shared task: Reliable Intelligence Identification on Vietnamese SNSs. With appropriate training techniques, our model is able to achieve 0.9392 ROC-AUC on public test set and the final version settles at top 2 ROC-AUC (0.9513) on private test set.
翻译:本文介绍了我们调整以变压器为基础的预先培训模式的方法,以适应越南SNS问题的可靠情报识别方法。我们还提出了一个模式,将贝特基预先培训模式与一些元数据特征(如评论数量、类似数量、SNS文件图像)相结合,以便改进VLSP共同任务的结果:越南SNS的可靠情报识别。在适当的培训技术下,我们的模型能够在公开测试组和最后版本中达到0.9392 ROC-AUC(公共测试组),并在私人测试组前2个ROC-AUC(0.9513)达到最后版本。